fips_table <- get_fips(datadir = datadir)
reopen.df <- get_reopendata(datadir=datadir)
alldat.df <- get_testdata(mindate = "2020-03-17")
reopen.df <- get_reopendata(datadir = datadir)
alldat.df$valueperM <- alldat.df$value*1e6/
fips_table[alldat.df$state,"pop"]
allcumdat.df <- get_cumul_testdata(mindate = "2020-03-17")
allcumdat.df$valueperM <- allcumdat.df$value*1e6/
fips_table[allcumdat.df$state,"pop"]
dat.df <- subset(alldat.df,state==statenow)
cumdat.df <- subset(allcumdat.df,state==statenow)
ggplot(dat.df)+geom_point(aes(x=numDate,y=value,color=variable))+
scale_y_log10()+
geom_vline(aes(linetype=ReopenType,xintercept=numDate),
data=subset(reopen.df,State.Abbr==statenow))+
ggtitle(paste(statenow,"Daily"))
ggplot(cumdat.df)+geom_point(aes(x=numDate,y=value,color=variable))+
scale_y_log10()+
geom_vline(aes(linetype=ReopenType,xintercept=numDate),
data=subset(reopen.df,State.Abbr==statenow))+
ggtitle(paste(statenow,"Cumulative"))
reopen.df$state <- reopen.df$State.Abbr
ggplot(alldat.df)+geom_point(aes(x=numDate,y=value,color=variable))+
scale_y_log10()+facet_wrap(~state)+
xlim(range(alldat.df$numDate))+
geom_vline(aes(linetype=ReopenType,xintercept=numDate),
data=reopen.df)+
ggtitle("Daily")
ggplot(allcumdat.df)+geom_point(aes(x=numDate,y=value,color=variable))+
scale_y_log10()+facet_wrap(~state)+
xlim(range(alldat.df$numDate))+
geom_vline(aes(linetype=ReopenType,xintercept=numDate),
data=reopen.df)+
ggtitle("Cumulative")
ggplot(alldat.df)+geom_point(aes(x=numDate,y=valueperM,color=variable))+
xlim(range(alldat.df$numDate))+
geom_vline(aes(linetype=ReopenType,xintercept=numDate),
data=reopen.df)+
scale_y_log10()+facet_wrap(~state)+
ggtitle("Daily")
ggplot(allcumdat.df)+geom_point(aes(x=numDate,y=valueperM,color=variable))+
xlim(range(alldat.df$numDate))+
geom_vline(aes(linetype=ReopenType,xintercept=numDate),
data=reopen.df)+
scale_y_log10()+facet_wrap(~state)+
ggtitle("Cumulative")
Set up MCSim file.
mdir <- "../MCSim"
source(file.path(mdir,"setup_MCSim.R"))
# Make mod.exe (used to create mcsim executable from model file)
makemod(mdir)
## The mod.exe had been created.
model_file<- "SEIR.reopen.model.R"
exe_file<-makemcsim(model_file,modeldir=modeldir,mdir="../MCSim")
## * Created executable program '../model/mcsim.SEIR.reopen.model.R.exe'.
Start time = 60. Texas population.
set.seed(314159)
in_file <- "SEIR_Testing.in.R"
out_dat <- mcsim(exe_file = exe_file,
in_file = in_file,
resultsdir = resultsdir)
## Execute: ./../model/mcsim.SEIR.reopen.model.R.exe ./SEIR_Testing.in.R ./sim.out
out_dat <- data.table(out_dat)
states_S.df <- melt(out_dat[,c("Time","S","S_C","Tot")],id.vars=1)
ggplot(states_S.df,aes(x=Time,y=value,color=variable))+geom_line() + scale_y_log10()+facet_wrap(~variable)+theme(legend.position = "none")
## Warning: Transformation introduced infinite values in continuous y-axis
states_E.df <- melt(out_dat[,c("Time","E","E_C")],id.vars=1)
ggplot(states_E.df,aes(x=Time,y=value,color=variable))+geom_line() + scale_y_log10()+facet_wrap(~variable)+theme(legend.position = "none")
## Warning: Transformation introduced infinite values in continuous y-axis
states_I.df <- melt(out_dat[,c("Time","I_U","I_C","I_T")],id.vars=1)
ggplot(states_I.df,aes(x=Time,y=value,color=variable))+geom_line() + scale_y_log10()+facet_wrap(~variable)+theme(legend.position = "none")
## Warning: Transformation introduced infinite values in continuous y-axis
states_R.df <- melt(out_dat[,c("Time","R_U","R_T","F_T")],id.vars=1)
ggplot(states_R.df,aes(x=Time,y=value,color=variable))+geom_line() + scale_y_log10()+facet_wrap(~variable)+theme(legend.position = "none")
## Warning: Transformation introduced infinite values in continuous y-axis
totals.df <- melt(out_dat[,c("Time","CumInfected", "CumPosTest", "CumDeath")],id.vars=1)
ggplot(totals.df,aes(x=Time,y=value,color=variable))+geom_line() + scale_y_log10()+facet_wrap(~variable)+theme(legend.position = "none")
## Warning: Transformation introduced infinite values in continuous y-axis
timedep.df <- melt(out_dat[,c("Time","ThetaFit","HygieneFit","FTraced", "lambda", "lambda_C", "rho_C", "delta", "c", "beta","Rt","Refft")],id.vars=1)
ggplot(timedep.df,aes(x=Time,y=value,color=variable))+geom_line() +facet_wrap(~variable,scales="free_y")+theme(legend.position = "none")
const.df <- melt(out_dat[,c("Time","NInit", "TIsolation", "R0", "c0", "TLatent", "TRecover", "IFR", "TStartTesting", "TauTesting", "TTestingRate", "TContactsTestingRate", "TestingCoverage", "TestSensitivity", "ThetaMin", "TauTheta", "PwrTheta", "HygienePwr", "FTraced0", "TPosTest", "TFatalDeath", "alpha", "kappa", "rho", "lambda0", "lambda0_C", "rho0_C","beta0")],id.vars=1)
## Warning in melt.data.table(out_dat[, c("Time", "NInit", "TIsolation", "R0", :
## 'measure.vars' [NInit, TIsolation, R0, c0, ...] are not all of the same type. By
## order of hierarchy, the molten data value column will be of type 'double'. All
## measure variables not of type 'double' will be coerced too. Check DETAILS in ?
## melt.data.table for more on coercion.
ggplot(const.df,aes(x=Time,y=value,color=variable))+geom_line()+scale_y_log10()+facet_wrap(~variable)+theme(legend.position = "none")
out_dat.tmp<-out_dat[,c("Time","N_pos","D_pos")]
names(out_dat.tmp)<-c("Time","positiveIncrease","deathIncrease")
obs.df <- melt(out_dat.tmp,id.vars=1)
ggplot(obs.df,aes(x=Time,y=value,color=variable))+geom_line() +
scale_y_log10(limits=c(1,NA)) +
geom_point(data=dat.df,aes(x=numDate,y=value,color=variable))
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 23 rows containing missing values (geom_path).
## Monte Carlo test
Counts how many integration failures occur using 5000 random parameter sets.
# Without integration
in_file0 <- "SEIR_Testing_MTC0.in.R"
out_dat0 <- mcsim(exe_file = exe_file,
in_file = in_file0,
out_file = file.path(resultsdir,"simMTC0.out"),
resultsdir = resultsdir,ignore.stdout = FALSE)
out_dat0 <- fread(file.path(resultsdir,"simMTC0.out"),select=1:25)
# out_mtc0.df <- melt(out_dat0,id.vars=1)
# out_mtc0.df$dointeg <- FALSE
# With integration
in_file <- "SEIR_Testing_MTC.in.R"
out_dat <- mcsim(exe_file = exe_file,
in_file = in_file,
out_file = file.path(resultsdir,"simMTC.out"),
resultsdir = resultsdir,ignore.stdout = FALSE)
out_dat <- fread(file.path(resultsdir,"simMTC.out"),select=1:25)
# ggpairs(log10(out_dat[,2:17]),
# lower = list(continuous = wrap("points", size=0.2,alpha=0.4)),
# title="Succeeded integration"
# )
integdat<-setdiff(out_dat0,out_dat)
# ggpairs(log10(integdat[,2:17]),
# lower = list(continuous = wrap("points", size=0.2,alpha=0.4)),
# title="Failed integration"
# )
nfail <- nrow(integdat)
print(nfail)
## [1] 0
if (nfail > 50) {
integdat$integ <- TRUE
tmp <- out_dat
tmp$integ <- FALSE
all_mtc <- rbind(integdat,tmp)
all_mtc[,2:22]<-log10(all_mtc[,2:22])
ggpairs(all_mtc[,2:23],
mapping = ggplot2::aes(color = integ,alpha=integ),
lower = list(continuous = wrap("points", size=0.2))
)
}
system(paste("rm",file.path(resultsdir,"simMTC.out")))
resultsdir <- "TX.val"
fips_table <- get_fips(datadir = datadir)
dat.df <- get_testdata()
cumdat.df <- get_cumul_testdata()
burnin <- 0.1
datadatemax <- "2020-04-30"
priorfile<-"SEIR.reopen_priors_MCMC.in.R"
statenow <- "TX"
popnow <- fips_table$pop[fips_table$Alpha.code==statenow]
prior_template <- make_infile_template(dat.df,
fips_table,
state_abbr=statenow,
usestatename = TRUE,
createdir = TRUE,
pathdir = resultsdir,
priordir = "../priors",
priorfile = gsub("MCMC","MTC",priorfile),
usemobility = FALSE,
mobilitydir = "../MobilityMetrics",
isprior = TRUE
)
prior_dat <- mcsim(exe_file = exe_file,
in_file = prior_template,
out_file = file.path(resultsdir,gsub(".in.R",".out",prior_template)),
resultsdir = resultsdir)
## Execute: ./../model/mcsim.SEIR.reopen.model.R.exe TX.val/SEIR_TX_MTC.in.R TX.val/SEIR_TX_MTC.out
infile_template <- make_infile_template(dat.df,
fips_table,
state_abbr=statenow,
usestatename = TRUE,
createdir = TRUE,
pathdir = resultsdir,
X_iter = "2000",
X_print = "10",
priordir = "../priors",
priorfile = priorfile,
datadatemax=datadatemax,
usemobility = FALSE,
mobilitydir = "../MobilityMetrics")
set.seed(exp(2))
out_dat <- mcsim(exe_file = exe_file,
in_file = infile_template,
resultsdir = resultsdir)#,ignore.stdout = TRUE)
## Execute MCMC: ./../model/mcsim.SEIR.reopen.model.R.exe TX.val/SEIR_TX_MCMC.in.R TX.val/SEIR_TX_MCMC1.out
## * Created 'TX.val/SEIR_TX_MCMC1.out'
## Execute MCMC Check: ./../model/mcsim.SEIR.reopen.model.R.exe TX.val/SEIR_TX_MCMC.in.R TX.val/SEIR_TX_MCMC1.out
## * Created 'TX.val/SEIR_TX_MCMC1.check.out' from the last iteration.
make_diagnostic(out_dat, subset(dat.df,state==statenow),
subset(cumdat.df,state==statenow), burnin=0.1,
pdfname=file.path(resultsdir,
paste0("Test.Validation.",
statenow,".pdf")))
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 6 rows containing missing values (geom_col).
## Warning: Removed 30 rows containing missing values (geom_path).
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1935 rows containing missing values (geom_point).
## quartz_off_screen
## 2
statenow <- "TX"
resultsdir <- "TX.pred"
fips_table <- get_fips(datadir = datadir)
dat.df <- get_testdata()
cumdat.df <- get_cumul_testdata()
burnin <- 0.1
popnow <- fips_table$pop[fips_table$Alpha.code==statenow]
priorfile<-"SEIR.reopen_state_priors_MCMC.in.R"
prior_template <- make_infile_template(dat.df,
fips_table,
state_abbr=statenow,
usestatename = TRUE,
createdir = TRUE,
pathdir = resultsdir,
priordir = "../priors",
priorfile = gsub("MCMC","MTC",priorfile),
usemobility = TRUE,
mobilitydir = "../MobilityMetrics",
isprior = TRUE
)
set.seed(exp(1))
prior_dat <- mcsim(exe_file = exe_file,
in_file = prior_template,
out_file = file.path(resultsdir,gsub(".in.R",".out",prior_template)),
resultsdir = resultsdir)
## Execute: ./../model/mcsim.SEIR.reopen.model.R.exe TX.pred/SEIR_TX_MTC.in.R TX.pred/SEIR_TX_MTC.out
infile_template <- make_infile_template(dat.df,
fips_table,
state_abbr=statenow,
usestatename = TRUE,
createdir = TRUE,
pathdir = resultsdir,
X_iter = "2000",
X_print = "10",
priordir = "../priors",
priorfile = priorfile,
datadatemax = "2020-06-20",
usemobility = TRUE,
mobilitydir = "../MobilityMetrics")
set.seed(exp(1))
out_dat <- mcsim(exe_file = exe_file,
in_file = infile_template,
resultsdir = resultsdir)#,ignore.stdout = TRUE)
## Execute MCMC: ./../model/mcsim.SEIR.reopen.model.R.exe TX.pred/SEIR_TX_MCMC.in.R TX.pred/SEIR_TX_MCMC1.out
## * Created 'TX.pred/SEIR_TX_MCMC1.out'
## Execute MCMC Check: ./../model/mcsim.SEIR.reopen.model.R.exe TX.pred/SEIR_TX_MCMC.in.R TX.pred/SEIR_TX_MCMC1.out
## * Created 'TX.pred/SEIR_TX_MCMC1.check.out' from the last iteration.
make_diagnostic(out_dat, subset(dat.df,state==statenow),
subset(cumdat.df,state==statenow), burnin=0.1,
pdfname=file.path(resultsdir,
paste0("Test.Prediction.",statenow,".pdf")))
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 6 rows containing missing values (geom_col).
## Warning: Removed 15 rows containing missing values (geom_path).
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1935 rows containing missing values (geom_point).
## quartz_off_screen
## 2
statenow <- "TX"
resultsdir <- "TX"
fips_table <- get_fips(datadir = datadir)
dat.df <- get_testdata()
cumdat.df <- get_cumul_testdata()
write.csv(fips_table,
file=file.path("FIPS_TABLE.csv"),
row.names = FALSE)
write.csv(dat.df,
file.path("DAILYTESTDATA.csv"),
row.names = FALSE)
write.csv(cumdat.df,
file.path("CUMULTESTDATA.csv"),row.names = FALSE)
burnin <- 0.1
popnow <- fips_table$pop[fips_table$Alpha.code==statenow]
priorfile<-"SEIR.reopen_state_priors_MCMC.in.R"
prior_template <- make_infile_template(dat.df,
fips_table,
state_abbr=statenow,
usestatename = TRUE,
createdir = TRUE,
pathdir = resultsdir,
priordir = "../priors",
priorfile = gsub("MCMC","MTC",priorfile),
usemobility = TRUE,
mobilitydir = "../MobilityMetrics",
isprior = TRUE
)
set.seed(exp(1))
prior_dat <- mcsim(exe_file = exe_file,
in_file = prior_template,
out_file = file.path(resultsdir,gsub(".in.R",".out",prior_template)),
resultsdir = resultsdir)
## Execute: ./../model/mcsim.SEIR.reopen.model.R.exe TX/SEIR_TX_MTC.in.R TX/SEIR_TX_MTC.out
infile_template <- make_infile_template(dat.df,
fips_table,
state_abbr=statenow,
usestatename = TRUE,
createdir = TRUE,
pathdir = resultsdir,
X_iter = "2000",
X_print = "10",
priordir = "../priors",
priorfile = priorfile,
datadatemax = "2020-06-20",
usemobility = TRUE,
mobilitydir = "../MobilityMetrics")
make_infiles(infile_template,
exe_file=exe_file,
chains=1:4,
useposterior=useposterior,
resultsdir=resultsdir,
randomseed=exp(1))
for (chainnum in 1:4) {
out_dat <- mcsim(exe_file = exe_file,
in_file = infile_template,
chainnum = chainnum,
resultsdir = resultsdir)#,ignore.stdout = TRUE)
}
## Execute MCMC: ./../model/mcsim.SEIR.reopen.model.R.exe TX/SEIR_TX_MCMC.in.R TX/SEIR_TX_MCMC1.out
## * Created 'TX/SEIR_TX_MCMC1.out'
## Execute MCMC Check: ./../model/mcsim.SEIR.reopen.model.R.exe TX/SEIR_TX_MCMC.in.R TX/SEIR_TX_MCMC1.out
## * Created 'TX/SEIR_TX_MCMC1.check.out' from the last iteration.
## Execute MCMC: ./../model/mcsim.SEIR.reopen.model.R.exe TX/SEIR_TX_MCMC.in.R TX/SEIR_TX_MCMC2.out
## * Created 'TX/SEIR_TX_MCMC2.out'
## Execute MCMC Check: ./../model/mcsim.SEIR.reopen.model.R.exe TX/SEIR_TX_MCMC.in.R TX/SEIR_TX_MCMC2.out
## * Created 'TX/SEIR_TX_MCMC2.check.out' from the last iteration.
## Execute MCMC: ./../model/mcsim.SEIR.reopen.model.R.exe TX/SEIR_TX_MCMC.in.R TX/SEIR_TX_MCMC3.out
## * Created 'TX/SEIR_TX_MCMC3.out'
## Execute MCMC Check: ./../model/mcsim.SEIR.reopen.model.R.exe TX/SEIR_TX_MCMC.in.R TX/SEIR_TX_MCMC3.out
## * Created 'TX/SEIR_TX_MCMC3.check.out' from the last iteration.
## Execute MCMC: ./../model/mcsim.SEIR.reopen.model.R.exe TX/SEIR_TX_MCMC.in.R TX/SEIR_TX_MCMC4.out
## * Created 'TX/SEIR_TX_MCMC4.out'
## Execute MCMC Check: ./../model/mcsim.SEIR.reopen.model.R.exe TX/SEIR_TX_MCMC.in.R TX/SEIR_TX_MCMC4.out
## * Created 'TX/SEIR_TX_MCMC4.check.out' from the last iteration.
system(paste("cp",file.path(functiondir,
"plot_parameter_results.R"),
resultsdir))
system(paste("cp",file.path(functiondir,
"run_batch_rhat_multicheck.R"),
resultsdir))
system(paste("cp",file.path(modeldir,basename(exe_file)),
file.path(resultsdir,
gsub(".exe","",basename(exe_file)))))
wd <- getwd()
setwd(resultsdir)
source("plot_parameter_results.R")
## [,1]
## GM_NInit.1. 1.278387
## GM_TIsolation.1. 1.007916
## GM_R0.1. 4.605071
## GM_c0.1. 1.002404
## GM_TLatent.1. 1.071191
## GM_TRecover.1. 1.757250
## GM_IFR.1. 1.266413
## GM_TStartTesting.1. 8.136230
## GM_TauTesting.1. 5.839326
## GM_TTestingRate.1. 1.144606
## GM_TContactsTestingRate.1. 1.010410
## GM_TestingCoverage.1. 1.073967
## GM_TestSensitivity.1. 1.008816
## GM_ThetaMin.1. 7.068529
## GM_TauTheta.1. 1.136048
## GM_PwrTheta.1. 1.010689
## GM_HygienePwr.1. 2.895599
## GM_FracTraced.1. 1.230270
## GM_TPosTest.1. 1.123036
## GM_TFatalDeath.1. 5.240737
## GM_TauS.1. 1.107615
## GM_rMax.1. 1.025155
## GM_TauR.1. 1.025190
## alpha_Pos.1. 1.130488
## alpha_Death.1. 1.035886
## LnPrior 1.852612
## LnData 4.100665
## LnPosterior 3.277557
## Warning in melt(priors[, 1:(ncol(priors) - 1)], id.vars = 1): The melt generic
## in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is deprecated, and this
## redirection is now deprecated as well. To continue using melt methods from
## reshape2 while both libraries are attached, e.g. melt.list, you can prepend
## the namespace like reshape2::melt(priors[, 1:(ncol(priors) - 1)]). In the next
## version, this warning will become an error.
source("run_batch_rhat_multicheck.R")
## [,1]
## GM_NInit.1. 1.278387
## GM_TIsolation.1. 1.007916
## GM_R0.1. 4.605071
## GM_c0.1. 1.002404
## GM_TLatent.1. 1.071191
## GM_TRecover.1. 1.757250
## GM_IFR.1. 1.266413
## GM_TStartTesting.1. 8.136230
## GM_TauTesting.1. 5.839326
## GM_TTestingRate.1. 1.144606
## GM_TContactsTestingRate.1. 1.010410
## GM_TestingCoverage.1. 1.073967
## GM_TestSensitivity.1. 1.008816
## GM_ThetaMin.1. 7.068529
## GM_TauTheta.1. 1.136048
## GM_PwrTheta.1. 1.010689
## GM_HygienePwr.1. 2.895599
## GM_FracTraced.1. 1.230270
## GM_TPosTest.1. 1.123036
## GM_TFatalDeath.1. 5.240737
## GM_TauS.1. 1.107615
## GM_rMax.1. 1.025155
## GM_TauR.1. 1.025190
## alpha_Pos.1. 1.130488
## alpha_Death.1. 1.035886
## LnPrior 1.852612
## LnData 4.100665
## LnPosterior 3.277557
setwd(wd)
datadatemax <- "2020-06-20"
folder<-"."
fips_table <- read.csv("FIPS_TABLE.csv",colClasses=c(
rep("character",4),rep("numeric",2)
))
statenow <- "TX"
scen_model_file<- "SEIR.scenarios.model.R"
scen_exe_file<-makemcsim(scen_model_file,modeldir=modeldir,mdir="../MCSim")
## * Created executable program '../model/mcsim.SEIR.scenarios.model.R.exe'.
output <- run_setpoints1(fips_table,
state_abbr=statenow,
TPrint=datadatemax,
pathdir=folder,
scenariosdir = "../scenarios",
scenariostemplate=
"SEIR.reopen_state_setpoints1_MCMC.in.R",
scenarioname = "OneTime",
nruns = 0,
keepoutfile = TRUE,
exe_file=scen_exe_file)
## Execute Setpoints: ./../model/mcsim.SEIR.scenarios.model.R.exe ././TX/SEIR_TX_OneTime.in ./TX/SEIR_TX_OneTime.out
scen.df <- data.frame(state=sort(rep(statenow,12)),
mu_C = rep(rep(c(1,1,2,2),3),length(statenow)),
mu_Lambda = rep(rep(c(1,2,1,2),3),length(statenow)),
DeltaDelta = rep(c(rep(0,4),rep(0.25,4),rep(-0.25,4)),length(statenow)),
stringsAsFactors = FALSE
)
scen.df$scenarioname <- paste("TimeSeries",scen.df$mu_C,scen.df$mu_Lambda,
scen.df$DeltaDelta,sep=".")
scen.df$scenariodesc <- paste0(scen.df$mu_C,"X Contact Tracing, ",
scen.df$mu_Lambda,"X Testing, ",
ifelse(sign(scen.df$DeltaDelta)==1,
paste0("+",100*scen.df$DeltaDelta,"%"),
ifelse(sign(scen.df$DeltaDelta)== -1,
paste0(100*scen.df$DeltaDelta,"%"),
ifelse(sign(scen.df$DeltaDelta)==0,
"Current",""
)))," Reopening")
pdf(file=file.path(folder,statenow,
"Scenarios_TestRuns.pdf"),height=4,width=6)
for (j in 1:nrow(scen.df)) {
scenrow<-scen.df[j,]
output <- run_setpoints(fips_table,
state_abbr=scenrow$state,
pathdir=folder,
scenariosdir="../scenarios",
scenariostemplate=
"SEIR.reopen_state_setpoints_MCMC.in.R",
scenarioname = scenrow$scenarioname,
nruns = 0,
mu_C = scenrow$mu_C,
mu_Lambda = scenrow$mu_Lambda,
DeltaDelta = scenrow$DeltaDelta,
rampuptime=14,
keepoutfile = FALSE,
exe_file=scen_exe_file)
plot_scenario(alldat.df, output$out_quant,scenrow$state,
logy=FALSE,
scenarioname = scenrow$scenariodesc)
}
## Execute Setpoints: ./../model/mcsim.SEIR.scenarios.model.R.exe ././TX/SEIR_TX_TimeSeries.1.1.0.in ./TX/SEIR_TX_TimeSeries.1.1.0.out
## Warning in melt.data.table(as.data.table(out_dat), id.vars = 1, variable.factor
## = FALSE): 'measure.vars' [GM_NInit, GM_TIsolation, GM_R0, GM_c0, ...] are not
## all of the same type. By order of hierarchy, the molten data value column will
## be of type 'double'. All measure variables not of type 'double' will be coerced
## too. Check DETAILS in ?melt.data.table for more on coercion.
## Warning: Removed 17 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_col).
## Execute Setpoints: ./../model/mcsim.SEIR.scenarios.model.R.exe ././TX/SEIR_TX_TimeSeries.1.2.0.in ./TX/SEIR_TX_TimeSeries.1.2.0.out
## Warning in melt.data.table(as.data.table(out_dat), id.vars = 1, variable.factor
## = FALSE): 'measure.vars' [GM_NInit, GM_TIsolation, GM_R0, GM_c0, ...] are not
## all of the same type. By order of hierarchy, the molten data value column will
## be of type 'double'. All measure variables not of type 'double' will be coerced
## too. Check DETAILS in ?melt.data.table for more on coercion.
## Warning: Removed 17 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_col).
## Execute Setpoints: ./../model/mcsim.SEIR.scenarios.model.R.exe ././TX/SEIR_TX_TimeSeries.2.1.0.in ./TX/SEIR_TX_TimeSeries.2.1.0.out
## Warning in melt.data.table(as.data.table(out_dat), id.vars = 1, variable.factor
## = FALSE): 'measure.vars' [GM_NInit, GM_TIsolation, GM_R0, GM_c0, ...] are not
## all of the same type. By order of hierarchy, the molten data value column will
## be of type 'double'. All measure variables not of type 'double' will be coerced
## too. Check DETAILS in ?melt.data.table for more on coercion.
## Warning: Removed 17 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_col).
## Execute Setpoints: ./../model/mcsim.SEIR.scenarios.model.R.exe ././TX/SEIR_TX_TimeSeries.2.2.0.in ./TX/SEIR_TX_TimeSeries.2.2.0.out
## Warning in melt.data.table(as.data.table(out_dat), id.vars = 1, variable.factor
## = FALSE): 'measure.vars' [GM_NInit, GM_TIsolation, GM_R0, GM_c0, ...] are not
## all of the same type. By order of hierarchy, the molten data value column will
## be of type 'double'. All measure variables not of type 'double' will be coerced
## too. Check DETAILS in ?melt.data.table for more on coercion.
## Warning: Removed 17 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_col).
## Execute Setpoints: ./../model/mcsim.SEIR.scenarios.model.R.exe ././TX/SEIR_TX_TimeSeries.1.1.0.25.in ./TX/SEIR_TX_TimeSeries.1.1.0.25.out
## Warning in melt.data.table(as.data.table(out_dat), id.vars = 1, variable.factor
## = FALSE): 'measure.vars' [GM_NInit, GM_TIsolation, GM_R0, GM_c0, ...] are not
## all of the same type. By order of hierarchy, the molten data value column will
## be of type 'double'. All measure variables not of type 'double' will be coerced
## too. Check DETAILS in ?melt.data.table for more on coercion.
## Warning: Removed 17 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_col).
## Execute Setpoints: ./../model/mcsim.SEIR.scenarios.model.R.exe ././TX/SEIR_TX_TimeSeries.1.2.0.25.in ./TX/SEIR_TX_TimeSeries.1.2.0.25.out
## Warning in melt.data.table(as.data.table(out_dat), id.vars = 1, variable.factor
## = FALSE): 'measure.vars' [GM_NInit, GM_TIsolation, GM_R0, GM_c0, ...] are not
## all of the same type. By order of hierarchy, the molten data value column will
## be of type 'double'. All measure variables not of type 'double' will be coerced
## too. Check DETAILS in ?melt.data.table for more on coercion.
## Warning: Removed 17 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_col).
## Execute Setpoints: ./../model/mcsim.SEIR.scenarios.model.R.exe ././TX/SEIR_TX_TimeSeries.2.1.0.25.in ./TX/SEIR_TX_TimeSeries.2.1.0.25.out
## Warning in melt.data.table(as.data.table(out_dat), id.vars = 1, variable.factor
## = FALSE): 'measure.vars' [GM_NInit, GM_TIsolation, GM_R0, GM_c0, ...] are not
## all of the same type. By order of hierarchy, the molten data value column will
## be of type 'double'. All measure variables not of type 'double' will be coerced
## too. Check DETAILS in ?melt.data.table for more on coercion.
## Warning: Removed 17 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_col).
## Execute Setpoints: ./../model/mcsim.SEIR.scenarios.model.R.exe ././TX/SEIR_TX_TimeSeries.2.2.0.25.in ./TX/SEIR_TX_TimeSeries.2.2.0.25.out
## Warning in melt.data.table(as.data.table(out_dat), id.vars = 1, variable.factor
## = FALSE): 'measure.vars' [GM_NInit, GM_TIsolation, GM_R0, GM_c0, ...] are not
## all of the same type. By order of hierarchy, the molten data value column will
## be of type 'double'. All measure variables not of type 'double' will be coerced
## too. Check DETAILS in ?melt.data.table for more on coercion.
## Warning: Removed 17 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_col).
## Execute Setpoints: ./../model/mcsim.SEIR.scenarios.model.R.exe ././TX/SEIR_TX_TimeSeries.1.1.-0.25.in ./TX/SEIR_TX_TimeSeries.1.1.-0.25.out
## Warning in melt.data.table(as.data.table(out_dat), id.vars = 1, variable.factor
## = FALSE): 'measure.vars' [GM_NInit, GM_TIsolation, GM_R0, GM_c0, ...] are not
## all of the same type. By order of hierarchy, the molten data value column will
## be of type 'double'. All measure variables not of type 'double' will be coerced
## too. Check DETAILS in ?melt.data.table for more on coercion.
## Warning: Removed 17 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_col).
## Execute Setpoints: ./../model/mcsim.SEIR.scenarios.model.R.exe ././TX/SEIR_TX_TimeSeries.1.2.-0.25.in ./TX/SEIR_TX_TimeSeries.1.2.-0.25.out
## Warning in melt.data.table(as.data.table(out_dat), id.vars = 1, variable.factor
## = FALSE): 'measure.vars' [GM_NInit, GM_TIsolation, GM_R0, GM_c0, ...] are not
## all of the same type. By order of hierarchy, the molten data value column will
## be of type 'double'. All measure variables not of type 'double' will be coerced
## too. Check DETAILS in ?melt.data.table for more on coercion.
## Warning: Removed 17 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_col).
## Execute Setpoints: ./../model/mcsim.SEIR.scenarios.model.R.exe ././TX/SEIR_TX_TimeSeries.2.1.-0.25.in ./TX/SEIR_TX_TimeSeries.2.1.-0.25.out
## Warning in melt.data.table(as.data.table(out_dat), id.vars = 1, variable.factor
## = FALSE): 'measure.vars' [GM_NInit, GM_TIsolation, GM_R0, GM_c0, ...] are not
## all of the same type. By order of hierarchy, the molten data value column will
## be of type 'double'. All measure variables not of type 'double' will be coerced
## too. Check DETAILS in ?melt.data.table for more on coercion.
## Warning: Removed 17 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_col).
## Execute Setpoints: ./../model/mcsim.SEIR.scenarios.model.R.exe ././TX/SEIR_TX_TimeSeries.2.2.-0.25.in ./TX/SEIR_TX_TimeSeries.2.2.-0.25.out
## Warning in melt.data.table(as.data.table(out_dat), id.vars = 1, variable.factor
## = FALSE): 'measure.vars' [GM_NInit, GM_TIsolation, GM_R0, GM_c0, ...] are not
## all of the same type. By order of hierarchy, the molten data value column will
## be of type 'double'. All measure variables not of type 'double' will be coerced
## too. Check DETAILS in ?melt.data.table for more on coercion.
## Warning: Removed 17 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_col).
dev.off()
## quartz_off_screen
## 2
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.1 (2019-07-05)
## os macOS Mojave 10.14.6
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/Chicago
## date 2020-07-03
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib source
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.6.0)
## backports 1.1.5 2019-10-02 [1] CRAN (R 3.6.0)
## bayesplot * 1.7.1 2019-12-01 [1] CRAN (R 3.6.0)
## bitops 1.0-6 2013-08-17 [1] CRAN (R 3.6.0)
## broom 0.5.3 2019-12-14 [1] CRAN (R 3.6.0)
## callr 3.4.0 2019-12-09 [1] CRAN (R 3.6.0)
## cellranger 1.1.0 2016-07-27 [1] CRAN (R 3.6.0)
## cli 2.0.0 2019-12-09 [1] CRAN (R 3.6.0)
## coda * 0.19-3 2019-07-05 [1] CRAN (R 3.6.0)
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## covid19us * 0.1.3 2020-04-29 [1] CRAN (R 3.6.1)
## crayon 1.3.4 2017-09-16 [1] CRAN (R 3.6.0)
## curl 4.3 2019-12-02 [1] CRAN (R 3.6.0)
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## DBI 1.1.0 2019-12-15 [1] CRAN (R 3.6.0)
## dbplyr 1.4.2 2019-06-17 [1] CRAN (R 3.6.0)
## desc 1.2.0 2018-05-01 [1] CRAN (R 3.6.0)
## devtools 2.2.1 2019-09-24 [1] CRAN (R 3.6.0)
## digest 0.6.23 2019-11-23 [1] CRAN (R 3.6.0)
## dplyr * 0.8.3 2019-07-04 [1] CRAN (R 3.6.0)
## ellipsis 0.3.0 2019-09-20 [1] CRAN (R 3.6.0)
## evaluate 0.14 2019-05-28 [1] CRAN (R 3.6.0)
## fansi 0.4.0 2018-10-05 [1] CRAN (R 3.6.0)
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## fs 1.3.1 2019-05-06 [1] CRAN (R 3.6.0)
## generics 0.0.2 2018-11-29 [1] CRAN (R 3.6.0)
## GGally * 1.4.0 2018-05-17 [1] CRAN (R 3.6.0)
## ggplot2 * 3.2.1 2019-08-10 [1] CRAN (R 3.6.0)
## ggridges 0.5.1 2018-09-27 [1] CRAN (R 3.6.0)
## glue 1.3.1 2019-03-12 [1] CRAN (R 3.6.0)
## gridExtra * 2.3 2017-09-09 [1] CRAN (R 3.6.0)
## gtable 0.3.0 2019-03-25 [1] CRAN (R 3.6.0)
## haven 2.2.0 2019-11-08 [1] CRAN (R 3.6.0)
## here * 0.1 2017-05-28 [1] CRAN (R 3.6.0)
## hms 0.5.2 2019-10-30 [1] CRAN (R 3.6.0)
## htmltools 0.4.0 2019-10-04 [1] CRAN (R 3.6.0)
## httr 1.4.1 2019-08-05 [1] CRAN (R 3.6.0)
## jsonlite * 1.6 2018-12-07 [1] CRAN (R 3.6.0)
## knitr 1.26 2019-11-12 [1] CRAN (R 3.6.0)
## labeling 0.3 2014-08-23 [1] CRAN (R 3.6.0)
## lattice 0.20-38 2018-11-04 [2] CRAN (R 3.6.1)
## lazyeval 0.2.2 2019-03-15 [1] CRAN (R 3.6.0)
## lifecycle 0.1.0 2019-08-01 [1] CRAN (R 3.6.0)
## lubridate 1.7.4 2018-04-11 [1] CRAN (R 3.6.0)
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## modelr 0.1.5 2019-08-08 [1] CRAN (R 3.6.0)
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## R6 2.4.1 2019-11-12 [1] CRAN (R 3.6.0)
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## Rcpp 1.0.3 2019-11-08 [1] CRAN (R 3.6.0)
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## stringi 1.4.3 2019-03-12 [1] CRAN (R 3.6.0)
## stringr * 1.4.0 2019-02-10 [1] CRAN (R 3.6.0)
## testthat 2.3.1 2019-12-01 [1] CRAN (R 3.6.0)
## tibble * 2.1.3 2019-06-06 [1] CRAN (R 3.6.0)
## tidyr * 1.0.2 2020-01-24 [1] CRAN (R 3.6.0)
## tidyselect 0.2.5 2018-10-11 [1] CRAN (R 3.6.0)
## tidyverse * 1.3.0 2019-11-21 [1] CRAN (R 3.6.0)
## usethis 1.5.1 2019-07-04 [1] CRAN (R 3.6.0)
## vctrs 0.2.1 2019-12-17 [1] CRAN (R 3.6.0)
## viridisLite 0.3.0 2018-02-01 [1] CRAN (R 3.6.0)
## withr 2.1.2 2018-03-15 [1] CRAN (R 3.6.0)
## xfun 0.11 2019-11-12 [1] CRAN (R 3.6.0)
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## yaml 2.2.0 2018-07-25 [1] CRAN (R 3.6.0)
## zeallot 0.1.0 2018-01-28 [1] CRAN (R 3.6.0)
##
## [1] /Users/wchiu/Library/R/3.6/library
## [2] /Library/Frameworks/R.framework/Versions/3.6/Resources/library